SCIENCE CHINA Information Sciences, Volume 62 , Issue 2 : 024101(2019) https://doi.org/10.1007/s11432-018-9590-5

Vehicle tracking by detection in UAV aerial video

More info
  • ReceivedMar 21, 2018
  • AcceptedSep 5, 2018
  • PublishedJan 2, 2019


There is no abstract available for this article.


This work was supported by National Key Research and Development Program of China (Grant No. 2016YFC0802500), National Natural Science Foundation of China (Grant No. 61532002), the 13th Five-Year Common Technology pre Research Program (Grant No. 41402050301-170441402065), and Science and Technology Mobilization Program of Dongguan (Grant No. KZ2017-06).


Videos and other supplemental documents.


[1] Wang K, Ke Y, Chen B M. Autonomous reconfigurable hybrid tail-sitter UAV U-Lion. Sci China Inf Sci, 2017, 60: 033201 CrossRef Google Scholar

[2] Li P, Yu X, Peng X. Fault-tolerant cooperative control for multiple UAVs based on sliding mode techniques. Sci China Inf Sci, 2017, 60: 070204 CrossRef Google Scholar

[3] He W, Huang H, Chen Y. Development of an autonomous flapping-wing aerial vehicle. Sci China Inf Sci, 2017, 60: 063201 CrossRef Google Scholar

[4] Kanistras K, Martins G, Rutherford M J, et al. A survey of unmanned aerial vehicles (UAVs) for traffic monitoring. In: Proceedings of International Conference on Unmanned Aircraft Systems, 2013. 221--234. Google Scholar

[5] Xiao J J, Cheng H, Sawhney H, et al. Vehicle detection and tracking in wide field-of-view aerial video. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. 679--684. Google Scholar

[6] Redmon J, Farhadi A. Yolov3: an incremental improvement. 2018,. arXiv Google Scholar

[7] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015. 91--99. Google Scholar

[8] Bewley A, Ge Z, Ott L, et al. Simple online and realtime tracking. In: Proceedings of IEEE International Conference on Image Processing (ICIP), 2016. 3464--3468. Google Scholar

[9] Bernardin K, Stiefelhagen R. Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J Image Video Process, 2008, 2008: 246309. Google Scholar

  • Figure 1

    (a) Working flow of the faster R-CNN; (b) working flow of tracking by detection; (c) detection results;protect łinebreak (d) visualized tracking results of a test video; (e) tracking results.